All Beings Are Equal in Open Set Recognition

In open-set recognition (OSR), a promising strategy is exploiting pseudo-unknown data outside given $K$ known classes as an additional $K$+$1$-th class to explicitly model potential open space. However, treating unknown classes without distinction is unequal for them relative to known classes due to the category-agnostic and scale-agnostic of the unknowns. This inevitably not only disrupts the inherent distributions of unknown classes but also incurs both class-wise and instance-wise imbalances between known and unknown classes. Ideally, the OSR problem should model the whole class space as $K$+$\infty$, but enumerating all unknowns is impractical. Since the core of OSR is to effectively model the boundaries of known classes, this means just focusing on the unknowns nearing the boundaries of targeted known classes seems sufficient. Thus, as a compromise, we convert the open classes from infinite to $K$, with a novel concept Target-Aware Universum (TAU) and propose a simple yet effective framework Dual Contrastive Learning with Target-Aware Universum (DCTAU). In details, guided by the targeted known classes, TAU automatically expands the unknown classes from the previous $1$ to $K$, effectively alleviating the distribution disruption and the imbalance issues mentioned above. Then, a novel Dual Contrastive (DC) loss is designed, where all instances irrespective of known or TAU are considered as positives to contrast with their respective negatives. Experimental results indicate DCTAU sets a new state-of-the-art.Comment: Accepted by the main track The 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024

Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling

BACKGROUND: It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. RESULTS: In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. CONCLUSION: We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs

Characterization and Evolution of microRNA Genes Derived from Repetitive Elements and Duplication Events in Plants

MicroRNAs (miRNAs) are a major class of small non-coding RNAs that act as negative regulators at the post-transcriptional level in animals and plants. In this study, all known miRNAs in four plant species (Arabidopsis thaliana, Populus trichocarpa, Oryza sativa and Sorghum bicolor) have been analyzed, using a combination of computational and comparative genomic approaches, to systematically identify and characterize the miRNAs that were derived from repetitive elements and duplication events. The study provides a complete mapping, at the genome scale, of all the miRNAs found on repetitive elements in the four test plant species. Significant differences between repetitive element-related miRNAs and non-repeat-derived miRNAs were observed for many characteristics, including their location in protein-coding and intergenic regions in genomes, their conservation in plant species, sequence length of their hairpin precursors, base composition of their hairpin precursors and the minimum free energy of their hairpin structures. Further analysis showed that a considerable number of miRNA families in the four test plant species arose from either tandem duplication events, segmental duplication events or a combination of the two. However, comparative analysis suggested that the contribution made by these two duplication events differed greatly between the perennial tree species tested and the other three annual species. The expansion of miRNA families in A. thaliana, O. sativa and S. bicolor are more likely to occur as a result of tandem duplication events than from segmental duplications. In contrast, genomic segmental duplications contributed significantly more to the expansion of miRNA families in P. trichocarpa than did tandem duplication events. Taken together, this study has successfully characterized miRNAs derived from repetitive elements and duplication events at the genome scale and provides comprehensive knowledge and deeper insight into the origins and evolution of miRNAs in plants

The Dichotomy in Degree Correlation of Biological Networks

Most complex networks from different areas such as biology, sociology or technology, show a correlation on node degree where the possibility of a link between two nodes depends on their connectivity. It is widely believed that complex networks are either disassortative (links between hubs are systematically suppressed) or assortative (links between hubs are enhanced). In this paper, we analyze a variety of biological networks and find that they generally show a dichotomous degree correlation. We find that many properties of biological networks can be explained by this dichotomy in degree correlation, including the neighborhood connectivity, the sickle-shaped clustering coefficient distribution and the modularity structure. This dichotomy distinguishes biological networks from real disassortative networks or assortative networks such as the Internet and social networks. We suggest that the modular structure of networks accounts for the dichotomy in degree correlation and vice versa, shedding light on the source of modularity in biological networks. We further show that a robust and well connected network necessitates the dichotomy of degree correlation, suggestive of an evolutionary motivation for its existence. Finally, we suggest that a dichotomous degree correlation favors a centrally connected modular network, by which the integrity of network and specificity of modules might be reconciled

Revisiting the variation of clustering coefficient of biological networks suggests new modular structure

<p>Abstract</p> <p>Background</p> <p>A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary.</p> <p>Results</p> <p>We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn’t show dependence of degree.</p> <p>Conclusions</p> <p>Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to “deterministic model” of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.</p

The distribution and conservation of repeat-related miRNAs in the four test plant species.

<p>(A) The percentage of RrmiRNAs and NRrmiRNAs in the <i>A. thaliana</i>, <i>P. trichocarpa</i>, <i>O. sativa</i> and <i>S. bicolor</i> genomes. (B) The percentage of repetitive element-related miRNAs located in intragenic regions compared to all known miRNAs in the corresponding genome. (C) The percentage of repeat-related miRNAs with differing degrees of conservation.</p

The results of conservation analysis for miRNA duplicated blocks in the four test plant species.

<p>ath: <i>A. thaliana</i>; ptc: <i>P. trichocarpa</i>; osa: <i>O. sativa</i>; sbi: <i>S. bicolor</i>.</p

Characterization and variation between RrmiRNAs and NRrmiRNAs.

<p>(A) The distribution of miRNA hairpin precursor sequence lengths in RrmiRNAs and NRrmiRNAs. (B) The G-C content in miRNA hairpin precursor sequences in RrmiRNAs and NRrmiRNAs (C) The MFEs for miRNA hairpin precursors in RrmiRNAs and NRrmiRNAs.</p

The number of segmental duplications and flanking conserved protein-coding genes within each block.

a<p>The number of observed duplicated blocks, containing miRNAs within the same family or within the different family, that have at least two flanking protein-coding gene surrounding miRNAs.</p>b<p>The percentage of observed duplicated blocks in each test species with respect to the total number of duplicated blocks.</p><p>ath: <i>A. thaliana</i>; ptc: <i>P. trichocarpa</i>; osa: <i>O. sativa</i>; sbi: <i>S. Bicolor.</i></p